find unique rows in a way compatible with numpy 1.12.1

Author: William de Vazelhes

metric_learn/itml.py

@@ -64,7 +64,7 @@ def _process_pairs(self, pairs, y, bounds):
     neg_pairs = neg_pairs[neg_no_ident]
     # init bounds
     if bounds is None:
-      X = np.unique(pairs.reshape(-1, pairs.shape[2]), axis=0)
+      X = np.vstack({tuple(row) for row in pairs.reshape(-1, pairs.shape[2])})
       self.bounds_ = np.percentile(pairwise_distances(X), (5, 95))
     else:
       assert len(bounds) == 2

metric_learn/lsml.py

@@ -50,7 +50,7 @@ def _prepare_quadruplets(self, quadruplets, weights):
       self.w_ = weights
     self.w_ /= self.w_.sum()  # weights must sum to 1
     if self.prior is None:
-      X = np.unique(pairs.reshape(-1, pairs.shape[2]), axis=0)
+      X = np.vstack({tuple(row) for row in pairs.reshape(-1, pairs.shape[2])})
       self.prior_inv_ = np.atleast_2d(np.cov(X, rowvar=False))
       self.M_ = np.linalg.inv(self.prior_inv_)
     else:

metric_learn/sdml.py

@@ -47,7 +47,7 @@ def _prepare_pairs(self, pairs, y):
                                       ensure_2d=False, allow_nd=True)
     # set up prior M
     if self.use_cov:
-      X = np.unique(pairs.reshape(-1, pairs.shape[2]), axis=0)
+      X = np.vstack({tuple(row) for row in pairs.reshape(-1, pairs.shape[2])})
       self.M_ = pinvh(np.cov(X, rowvar = False))
     else:
       self.M_ = np.identity(pairs.shape[2])